Multifractal roots of suprapostural dexterity

Kelty-Stephen, Damian G.; Lee, I. Chieh; Carver, Nicole S.; Newell, Karl M.; Mangalam, Madhur

doi:10.1016/j.humov.2021.102771

Cited by 21 publications

(26 citation statements)

References 132 publications

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“…With the rise of the fractal, multifractal, and other multi-scaled methodologies, an older tradition of using standard deviation to measure variability has fallen by the wayside (Goldberger et al, 2002; Lipsitz, 2004; Manor & Lipsitz, 2013), building a supposed tension between SD and fractal or multifractal types of variability. For instance, the sustained success of fractal and multifractal-nonlinearity measures in explaining the complexity of adaptive use of perception for action in movement and psychological sciences (Hasselman, 2015; Ihlen & Vereijken, 2013; Kardan et al, 2020; Kelty-Stephen et al, 2021; Vaz et al, 2020) has cast a regrettable shadow over longer-lived descriptors. For instance, one group of psychologists using both nonlinear and linear analyses to examine postural sway referred to SD as an example of “relatively crude parameters” (Stoffregen et al, 2013, p. 124).…”

Section: Discussionmentioning

confidence: 99%

“…The second implication of the present results is the confirmation and extension of an older observation that task demands accentuate the potential benefits of multifractal nonlinearity. Prior work showed that the capacity for multifractal nonlinearity to stabilize suprapostural dexterity in a visual-attention task increased with the accommodative demand (Kelty-Stephen et al, 2021). In the present work, increased task demand accentuated the effect of multifractal nonlinearity.…”

Section: Discussionmentioning

confidence: 99%

“…Exploration is always relative to existing constraints, and exploration is employed to determine how or whether to replace old, outdated constraints with new ones (Raja & Anderson, 2021). Similarly, multifractal nonlinearity can assist or hinder goal attainment depending on the task or movement constraints (Jacobson et al, 2021; Kelty-Stephen et al, 2021). Multifractal nonlinearity only refers to the capacity for variability when shifting between scales.…”

Section: Discussionmentioning

confidence: 99%

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Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

et al. 2023

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An adaptive response to unexpected perturbations requires near-term and long-term adjustments over time. We used multifractal analysis to test how nonlinear interactions across timescales might support an adaptive response following an unpredictable perturbation. We reanalyzed torque data from 44 young and 24 older adults who performed a single-leg squat task challenged by an unexpected mechanical perturbation and a secondary visual-cognitive task. We report three findings: (a) multifractal nonlinearity interacted with pre-perturbation torque production and task error to presage greater pre-voluntary feedforward increases and greater voluntary reductions, respectively, in post-perturbation task error; (b) multifractal nonlinearity presaged relatively smaller task error than standard deviations of both pre-perturbation torques and pre-perturbation task error; and (c) increased task demand (e.g., age-related changes in dexterity and dual-task settings) led to multifractal nonlinearity presaging reduced task error. All these results were consistent with our expectations, except that a pre-perturbation knee torque-dependent increase in post-perturbation task error appeared later for older than for younger participants. This correlational multifractal modeling offered theoretical clarity on the possible roles of nonlinear interactions across timescales, moderating both feedforward and feedback processes, and presaging greater stability when the standard deviation is relatively large and task demands are strong. Thus, multifractal nonlinearity usefully describes movement variability even when paired with classical descriptors like the standard deviation. We discuss potential insights from these findings for understanding suprapostural dexterity and developing rehabilitative interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

et al. 2023

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“…Multifractality arose from a long history of scientific curiosity about how fluid processes generate complex patterns (Richardson, 1926;Turing, 1952) and remains one of the leading ways to model fluid, nonlinear processes-as initially intended in hydrodynamics (Schertzer & Lovejoy, 2004) and more recently as a framework for understanding the fluid-structure of perception, action, and cognition (Dixon et al, 2012;Kelty-Stephen, 2017;Kelty-Stephen et al, 2021). In what follows, we unpack both what multifractality is and why it is helpful for quantifying nonlinear changes in series, with specific examples from perception, action, and cognition.…”

Section: Multifractality: a Type Of Nonlinearity For Modeling Process...mentioning

confidence: 99%

Multifractal test for nonlinearity of interactions across scales in time series

Kelty-Stephen¹,

Lane²,

Bloomfield³

et al. 2022

Preprint

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The creativity and emergence of biological and psychological behavior tend to be nonlinear—biological and psychological measures contain degrees of irregularity. The linear model might fail to reduce these measurements to a sum of independent random factors (yielding a stable mean for the measurement), implying nonlinear changes over time. The present work reviews some of the concepts implicated in nonlinear changes over time and details the mathematical steps involved in their identification. It introduces multifractality as a mathematical framework helpful in determining whether and to what degree the measured series exhibits nonlinear changes over time. These mathematical steps include multifractal analysis and surrogate data production for resolving when multifractality entails nonlinear changes over time. Ultimately, when measurements fail to fit the structures of the traditional linear model, multifractal modeling allows making those nonlinear excursions explicit, that is, to come up with a quantitative estimate of how strongly events may interact across timescales. This estimate may serve some interests as merely a potentially statistically significant indicator, but we suspect that this estimate might serve more generally as a predictor of perceptuomotor or cognitive performance.

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“…Exploiting the Fitts task as an experimental attempt, a task in which performance under critical time pressure is an essential foundation, it has been shown that global constraints can produce changes in the fine-scale dynamics of the hand trajectory ( Wijnants et al, 2012 ), interpretatively captured by a short-scale multifractal analysis ( Bell et al, 2019 ). The main intuition is that a fractally scaled measurement inherently exhibits a scale-invariant decay of variability, so that short-scale behaviors demonstrate close correlation with the longer-range or more global constraints of a task ( Palatinus et al, 2013 ; Anastas et al, 2014 ; Mangalam et al, 2020a , b , c ; Kelty-Stephen et al, 2021 ). Thus, the multiple fractal results obtained from the analysis of densely sampled movements provide insight into the hierarchy of cross-scale interactions of the movement system.…”

Section: Introductionmentioning

confidence: 99%

Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System

Pratviel

Deschodt-Arsac

Larrue³

et al. 2021

Front. Physiol.

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Beyond apparent simplicity, visuomotor dexterity actually requires the coordination of multiple interactions across a complex system that links the brain, the body and the environment. Recent research suggests that a better understanding of how perceptive, cognitive and motor activities cohere to form executive control could be gained from multifractal formalisms applied to movement behavior. Rather than a central executive “talking” to encapsuled components, the multifractal intuition suggests that eye-hand coordination arises from multiplicative cascade dynamics across temporal scales of activity within the whole system, which is reflected in movement time series. Here we examined hand movements of sport students performing a visuomotor task in virtual reality (VR). The task involved hitting spatially arranged targets that lit up on a virtual board under critical time pressure. Three conditions were compared where the visual search field changed: whole board (Standard), half-board lower view field (LVF) and upper view field (UVF). Densely sampled (90 Hz) time series of hand motions captured by VR controllers were analyzed by a focus-based multifractal detrended fluctuation analysis (DFA). Multiplicative rather than additive interactions across temporal scales were evidenced by testing comparatively phase-randomized surrogates of experimental series, which confirmed nonlinear processes. As main results, it was demonstrated that: (i) the degree of multifractality in hand motion behavior was minimal in LVF, a familiar visual search field where subjects correlatively reached their best visuomotor response times (RTs); (ii) multifractality increased in the less familiar UVF, but interestingly only for the non-dominant hand; and (iii) multifractality increased further in Standard, for both hands indifferently; in Standard, the maximal expansion of the visual search field imposed the highest demand as evidenced by the worst visuomotor RTs. Our observations advocate for visuomotor dexterity best described by multiplicative cascades dynamics and a system-wide distributed control rather than a central executive. More importantly, multifractal metrics obtained from hand movements behavior, beyond the confines of the brain, offer a window on the fine organization of control architecture, with high sensitivity to hand-related control behavior under specific constraints. Appealing applications may be found in movement learning/rehabilitation, e.g., in hemineglect people, stroke patients, maturing children or athletes.

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Multifractal roots of suprapostural dexterity

Cited by 21 publications

References 132 publications

Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

Multifractal Nonlinearity Moderates Feedforward and Feedback Responses to Suprapostural Perturbations

Multifractal test for nonlinearity of interactions across scales in time series

Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System

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